However, only unlabeled data is available in many real-world applications. It is a common challenge that machine can learn knowledge well enough in one domain and then answer questions in other domains without any labels. Unfortunately, the generalization capabilities of some existing RC neural models were proven to be weak across different datasets [1]}. In fact, the same conclusion can be drawn for BERT according to our experiment, e.g., the performance drops on CNN dataset using the model trained on SQuAD. Therefore, studies to eliminate such performance gaps between various datasets deserve effort.
